Making human immune systems more interpretable through systems immunology
Authors: Rikard Forlin, Anna James, Petter Brodin
Journal: Trends in Immunology (2023)

Systems immunology is the study of cell–cell interactions in the immune system, their regulatory functions, and the emergent properties of immune responses.We posit that, despite large interindividual variation, cell composition is nonrandom, and understanding its rules will make human immune systems more predictable in the future.A growing number of computational tools exists for the inference of cell–cell relationships, but no 'gold standard' methods exist, and statistical power is often limiting.Minimizing the technical sources of variance by optimal experimental design and sampling procedures allows for more biological 'signals' to be discovered. A systems approach is necessary to comprehend the complexity of an organism's immune system since there is an underlying shared mechanism of cell–cell relationships. Better inferences of cell–cell interactions are essential to increase our understanding of immune responses for various health conditions. A well-planned longitudinal sampling of whole blood is vital in translating in vitro findings to in vivo biology, allowing the systematic tracking of immune system responses over time, and facilitating the development of more accurate models with improved predictability for immune system behaviors. Imagine standing in front of the famous water lilies painting by Monet. Gazing from afar, you admire the carefully chosen colors and the beautiful scenery. However, as you move closer, expecting to see more detail, the patterns dissolve into a disorganized collection of brush strokes, and it becomes difficult to distinguish the water lilies that were so clear from a distance. Just as Monet's paintings are composed of countless small strokes that combine to form a beautiful painting, the immune system is composed of many individual cells, proteins, and molecules that represent a complex and intricate network of interacting agents. Now, if you are an aspiring artist, you must stand close to the painting and study the individual brush strokes to understand the painting, and how the strokes combine to make up the spectacular network of color. But it is just as important to take a step back, view the whole picture, and think to yourself 'what does this image represent?', examining all the components in the painting to truly understand the artist's motive and reasoning. Like a painting that consists of multiple elements, the immune system consists of many different components and cell populations with specialized functions. Achieving balance in these different populations is key to a healthy immune-system function and the overall health of an individual; indeed, these cell populations activate and inhibit each other, compete for growth factors and ligands, move in and out of compartmentalized organs with different regulatory logic and, while doing so, must maintain their regulatory balance and tune their thresholds for activation to remain tolerant to surrounding cells and tissues, yet responsive to relative changes in potentially harmful processes and targets [1.Parkin J. Cohen B. An overview of the immune system.Lancet. 2001; 357: 1777-1789Abstract Full Text Full Text PDF PubMed Scopus (791) Google Scholar, 2.Brodin P. et al.NK cell education: not an on-off switch but a tunable rheostat.Trends Immunol. 2009; 4: 143-149Abstract Full Text Full Text PDF Scopus (188) Google Scholar, 3.Grossman Z. Paul W.E. Dynamic tuning of lymphocytes: physiological basis, mechanisms, and function.Annu. Rev. 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Therefore, to understand the higher-order functions of this complex system, comprehensive analyses involving all cell populations are simultaneously required. Only then can we fully understand the consequences of activating one or several cell populations on the system, just like taking a step back to see the water lilies more clearly. In this opinion article, we introduce the concept of systems immunology and discuss important factors that must be considered, both methodological and analytical, for the successful application of this strategy. We also present examples of the clinical impact that systems immunology can have, our general hypothesis being that only a holistic approach can provide a full understanding of immune system function in health and disease. The fact that the immune system is structured as a CAS does not mean that the system lacks order or structure among its various components. There are certainly logical regulatory mechanisms at play as well, although most of these remain unknown. From an evolutionary perspective, our survival in an environment full of microbes has depended on a potent defense system, and microbes have played a dominant role in shaping it [7.Quintana-Murci L. et al.Immunology in natura: clinical, epidemiological and evolutionary genetics of infectious diseases.Nat. Immunol. 2007; 8: 1165-1171Crossref PubMed Scopus (144) Google Scholar]. Recent advances in single-cell biology have equipped us with the tools to perform systems immunology analyses capturing all the immune-cell populations present within a tissue sample and some functional properties of each cell, thereby allowing us to examine relationships among immune-cell populations at scale for the first time (Figure 1A, Key figure ). When seeing correlated changes among two cell populations, these could either indicate shared or coordinated responses or a shared influence by a third influencing cell population (Figure 1B). Such possible relationships are impossible to disentangle without further mechanistic experiments, although longitudinal monitoring of blood, when possible, permits more causal modeling of cell–cell relationships from time series data [8.Lakshmikanth T. et al.Human immune system variation during 1 year.Cell Rep. 2020; 32107923Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar]. Our laboratory's internal data illustrate the nonrandom and reproducible correlation structure among cell types. For illustration purposes: in preliminary work (which remains to be validated), we analyzed 1644 blood samples from healthy individuals using a 56-parameter mass cytometry panel; we noticed strong correlations between the frequencies of multiple cell types in circulation (Figure 1C) (Forlin et al., unpublished). For example, there is a strong inverse correlation between neutrophil abundance and the abundance of natural killer (NK) cells and B cells, and conversely, strong positive correlations between plasmacytoid dendritic cells (pDCs) and both eosinophils and monocytes (Figure 1C). This finding suggests that shared regulatory mechanisms exist among these various cell types and among individuals, preventing a defense system in complete chaos. This finding is particularly interesting because the immune system is unique in each individual, shaped by both heritable and nonheritable factors (such as the environment and upbringing, microbiota, and age) [9.Carr E.J. et al.The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 4: 461-468Crossref Scopus (188) Google Scholar, 10.Reichert T. et al.Lymphocyte subset reference ranges in adult Caucasians.Clin. Immunol. Immunopathol. 1991; 2: 190-208Crossref Scopus (289) Google Scholar, 11.Tollerud D.J. et al.The influence of age, race, and gender on peripheral blood mononuclear-cell subsets in healthy nonsmokers.J. Clin. Immunol. 1989; 3: 214-222Crossref Scopus (142) Google Scholar, 12.Hill D.L. et al.Immune system development varies according to age, location, and anemia in African children.Sci. Transl. 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One of the major goals of systems immunology is to increase the predictability of the immune system, and thereby contribute to the advancement of personalized medicine, vaccine development, and other clinical therapies. It can also provide a better understanding of the pathology of various immune-related diseases, such as cancer, as well as autoimmune and infectious diseases [20.Germain R.N. Schwartzberg P.L. The human condition: an immunological perspective.Nat. Immunol. 2011; 5: 369-372Crossref Scopus (16) Google Scholar,21.Germain R.N. Will systems biology deliver its promise and contribute to the development of new or improved vaccines?: what really constitutes the study of "systems biology" and how might such an approach facilitate vaccine design.Cold Spring Harb. Perspect. Biol. 2018; 10a033308Crossref PubMed Scopus (11) Google Scholar]. Numerous advances have been made towards this goal in recent years. For example, one study [22.Kaczorowski K.J. et al.Continuous immunotypes describe human immune variation and predict diverse responses.Proc. Natl. Acad. Sci. U. S. A. 2017; 114: E6097-E6106Crossref PubMed Scopus (71) Google Scholar] showed that by using the baseline composition of immune cells present in preparations of human peripheral blood mononuclear cells (PBMCs), three latent vectors describing the variance in immune-cell composition were predictive of functional responses – measured as phosphorylation of signal transducer and activator of transcription (STAT)1, STAT3, and STAT5 – to a variety of cytokines – IL-2, IL-6, IL-7, IL-10, IL-21, interferon (IFN)-α, and IFN-γ – exogenously added to PBMCs in vitro. Extensive research has also been dedicated to defining an immune state that can predict antibody responses following vaccination, and significant progress has been made in using human baseline blood-cell populations – such as longitudinally stable B cell populations— to predict antibody concentrations after influenza virus vaccination [measured as an adjusted maximum fold change (adjMFC) that does not depend on pre-existing antibody concentrations] [18.Tsang J.S. et al.Global analyses of human immune variation reveal baseline predictors of postvaccination responses.Cell. 2014; 2: 499-513Abstract Full Text Full Text PDF Scopus (295) Google Scholar,23.Nakaya H.I. et al.Systems analysis of immunity to influenza vaccination across multiple years and in diverse populations reveals shared molecular signatures.Immunity. 2015; 6: 1186-1198Abstract Full Text Full Text PDF Scopus (196) Google Scholar,24.Kotliarov Y. et al.Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus.Nat. 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These results further indicated that nonclassical monocytes and myeloid dendritic cells were likely sources of this prevaccination transcriptional signature when compared with single-cell (sc) mRNA-sequencing datasets [25.Fourati S. et al.Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination.Nat. Immunol. 2022; 12: 1777-1787Crossref Scopus (7) Google Scholar]. Recently, other work [26.Mulè M.P. et al.Multiscale integration of human and single-cell variations reveals unadjuvanted vaccine high responders are naturally adjuvanted.medRxiv. 2023; (Published online March 20, 2023. https://doi.org/10.1101/2023.03.20.23287474)PubMed Google Scholar] showed that influenza virus vaccine-induced transcriptional perturbations in various cell types varied between high and low responders. These included myeloid dendritic cells and nonclassical monocytes in blood tissues; thus, these were again found to be two of the key drivers of the postvaccination kinetics of high responders, as well as classical monocytes and mucosal associated invariant T (MAIT) cells. In the context of responses towards influenza virus or yellow fever virus vaccinations, one group [24.Kotliarov Y. et al.Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus.Nat. 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This transcriptome signature was proposed to serve as an indicator of the immune system's 'set point' and was predictive of higher antibody responses to either one of the vaccinations among healthy individuals, thus enabling the identification of high and low responders before vaccination, suggesting a future for personalized vaccination based on prevaccine immune states. The same signature also correlated with the magnitude of plasmablast-associated flares in a cohort consisting of patients with systemic lupus erythematosus, when evaluated during times of low or no disease activity. This suggested that transcriptional gene signatures should not be evaluated as isolated predictive instances, but rather that similar transcriptional mechanisms between inflammatory activities might enable a predictive power to a range of disease or vaccination states. Even though immune system characteristics vary between individuals, the mechanisms underlying certain cell–cell interactions and relationships seem to be common to all. These shared mechanisms enable the identification of transcriptomic, antibody, cytokine, or cell-population-frequency signatures that are shared across individuals. These can help predict the behavior of the immune system and lead to valid conclusions concerning the system as a whole, which is one of the ultimate quests of systems immunology. The mechanisms underlying these shared immunological properties remain unidentified. The immune system is a highly interactive system with continuously active communication pathways, some of which are general for a range of vaccines and diseases. The increased availability and technical advancements in single-cell transcriptomic data have led to an increasing interest in inferencing cell–cell communications. Indeed, there is a growing number of computational tools highlighting these pathways/interactions [27.Jin S. et al.Inference and analysis of cell–cell communication using CellChat.Nat. Commun. 2021; 12: 1088Crossref PubMed Scopus (756) Google Scholar, 28.Efremova M. et al.CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes.Nat. Protoc. 2020; 4: 1484-1506Crossref Scopus (937) Google Scholar, 29.Browaeys R. et al.NicheNet: modeling intercellular communication by linking ligands to target genes.Nat. 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However, the basis for the prior knowledge and the methods used to compute cell–cell interaction scores differ among different strategies [27.Jin S. et al.Inference and analysis of cell–cell communication using CellChat.Nat. Commun. 2021; 12: 1088Crossref PubMed Scopus (756) Google Scholar, 28.Efremova M. et al.CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes.Nat. Protoc. 2020; 4: 1484-1506Crossref Scopus (937) Google Scholar, 29.Browaeys R. et al.NicheNet: modeling intercellular communication by linking ligands to target genes.Nat. 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Future research to provide a reliable understanding of cell–cell interactions is necessary and would be of great value to systems immunology. Informational exchange between two or more cell types establishes a cell circuit [33.Hart Y. et al.Design principles of cell circuits with paradoxical components.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 8346-8351Crossref PubMed Scopus (56) Google Scholar,34.Zhou X. et al.Circuit design features of a stable two-cell system.Cell. 2018; 4: 744-757.e17Abstract Full Text Full Text PDF Scopus (176) Google Scholar], and even in a basic two-cell system, there are multiple potential circuit topologies. This was explored in a study [34.Zhou X. et al.Circuit design features of a stable two-cell system.Cell. 2018; 4: 744-757.e17Abstract Full Text Full Text PDF Scopus (176) Google Scholar] where in vitro cellular model systems were created to simulate growth factor exchange between macrophages and fibroblasts, in which the two cell types reached stability with respect to the macrophage–fibroblast population ratio. This population ratio was also resilient to perturbations, such as added colony-stimulating factor 1 (CSF1), platelet-derived growth factor B (PDGFB), or additional macrophages, despite the macrophages being below their carrying capacity (as is often the case with in vivo biology). As such, the authors were able to model exchanges of information between two cell types in an elegant and controlled model system [34.Zhou X. et al.Circuit design features of a stable two-cell system.Cell. 2018; 4: 744-757.e17Abstract Full Text Full Text PDF Scopus (176) Google Scholar]. Others have also developed a unique method: the scalable arrayed multivalent extracellular interaction screen (SAVEXIS) to screen for protein-binding interactions between recombinant extracellular domains [35.Shilts J. et al.A physical wiring diagram for the human immune system.Nature. 2022; 608: 397-404Crossref PubMed Scopus (17) Google Scholar]. They found that approximately 57% of human immune receptor binding pairs are unique, meaning that neither protein had any other binding partner. This exclusivity phenomenon was more prevalent in proteins generally recognized for signaling roles, such as CSF1 and its receptor, and BST2-LILRA4 (bone marrow stromal antigen 2–leukocyte immunoglobulin-like receptor subfamily A member 4) binding, while the largest interconnected groups (sharing several binding partners) contained integrins and other adhesion molecules. From this, the investigators also built a model that, while being simplistic, could infer relative frequencies at which human immune cells physically interacted. The model was accurate enough to be consistent with published empirical data on the frequency of ex vivo physical cell–cell interactions among human peripheral mononuclear leukocytes [36.Vladimer G.I. et al.Global survey of the immunomodulatory potential of common drugs.Nat. Chem. Biol. 2017; 13: 681-690Crossref PubMed Scopus (40) Google Scholar], thus enabling predictions for the behavior of a collection of leukocytes. They also found that immune activation changed the strength of cellular interactions. Higher-affinity interactions predominated in an inflamed state, and transient interactions dominated in a resting state (possibly supporting the need for dynamic responses when sensing potential threats) [35.Shilts J. et al.A physical wiring diagram for the human immune system.Nature. 2022; 608: 397-404Crossref PubMed Scopus (17) Google Scholar]. Results from a recent publication [37.Kramer B.A. et al.Multimodal perception links cellular state to decision-making in single cells.Science. 2022; 377: 642-648Crossref PubMed Scopus (8) Google Scholar] suggest that cellular decision-making is influenced not only by input signals and information about cell surroundings (such as growth factors) but also by cellular states (i.e., physicochemical state) which was proposed to be at least as important as the surroundings for how cells interact. Thus, when creating interactive network models of the immune system, it is important to consider the state and condition of each node (subset of cells). Given a certain input (such as receiving growth factors), the response/outputs from a subset of cells can therefore also give nonredundant information about the cellular state, such as whether they are in an inflammatory or resting state. It is tempting to imagine a future where results such as these [34.Zhou X. et al.Circuit design features of a stable two-cell system.Cell. 2018; 4: 744-757.e17Abstract Full Text Full Text PDF Scopus (176) Google Scholar,35.Shilts J. et al.A physical wiring diagram for the human immune system.Nature. 2022; 608: 397-404Crossref PubMed Scopus (17) Google Scholar,37.Kramer B.A. et al.Multimodal perception links cellular state to decision-making in single cells.Science. 2022; 377: 642-648Crossref PubMed Scopus (8) Google Scholar] combined measurements could enable predictions of which receptor interactions might occur and what downstream outputs will follow when a cell is in a certain state. Thereby, a circuit design of all agents involved can be formed. Although much remains to be discovered within this area, and current models are often simplistic, further investigation into the quantitative design of cell–cell relationships to predict the behavior of cells, or subsets of cells, will be of great value for achieving a true systems-level understanding of immune functions. A key aspect of the aforementioned modeling approaches is that the accuracy of a model's translation to in vivo biology relies on the underlying data used as input in the model. Therefore, it is vital to evaluate the quality of the data used to inform a model and to ensure that it is appropriate for the intended application and output. A crucial factor is the statistical power, which is often too low when numbers of measurements made far exceed the samples collected. It is essential to minimize any technical sources of variation in the data to maximize the chances of capturing biological signals of interest given these statistical power limitations. Our recent developments have shown that the preparation of PBMCs is a major source of technical variation when comparing cell frequency measurements by mass cytometry to whole blood samples, as well as stimulation-induced cytokine responses measured by fluorescent bead assays [38.Brodin P. et al.A call for blood – in human immunology.Immunity. 2019; 6: 1335-1336Abstract Full Text Full Text PDF Scopus (25) Google Scholar]. PBMC preparations also lead to the removal of neutrophils, the largest cell population in blood with important regulatory and effector functions during infections and inflammatory conditions. Also, platelets and plasma are lost during PBMC preparation which can impact the variability in cell composition and concentrations of plasma proteins which mediate many cell–cell regulatory relationships. Consequently, functional responses to stimulation in whole blood may be more reproducible and physiological than PBMC stimulation cultures [38.Brodin P. et al.A call for blood – in human immunology.Immunity. 2019; 6: 1335-1336Abstract Full Text Full Text PDF Scopus (25) Google Scholar, 39.Duffy D. et al.Standardized whole blood stimulation improves immunomonitoring of induced immune responses in multi-center study.Clin. Immunol. 2017; 183: 325-335Crossref PubMed Scopus (45) Google Scholar, 40.Appay V. et al.Immuno-monitoring of CD8+ T cells in whole blood versus PBMC samples.J. Immunol. Methods. 2006; 309: 192-199Crossref PubMed Scopus (40) Google Scholar]. All in all, because PBMC populations exist only outside of the organism – in either test tubes or culture dishes – we posit that whole blood is the most representative tissue for blood analyses and provides a more representative depiction of the in vivo environment. Similarly, blood analyses are not ideally representative of tissue-specific contexts and immune-cell interactions with stromal cells, or even other immune cells residing in specific tissues [41.Weisberg S.P. et al.Tissue-specific immunity for a changing world.Cell. 2021; 6: 1517-1529Abstract Full Text Full Text PDF Scopus (28) Google Scholar]. To overcome this limitation of blood-cell analyses, recent developments in organoids (such as lymphoid-cell organoids) for modeling adaptive immune responses holds important promise for systems immunology investigations in vitro and can directly enable mechanistic studies in human cells [42.Wagar L.E. et al.Modeling human adaptive immune responses with tonsil organoids.Nat. Med. 2021; 1: 125-135Crossref Scopus (82) Google Scholar]. Moreover, longitudinal sampling of living patients or longitudinal analyses of organoid cultures can capture variability over time and help distinguish technical sources of variation and measurement noise from biological fluctuations of interest, and in response to stimuli. Examples of biological fluctuations include circadian rhythms – and menstrual cycle differences in reproductive females – with possible impact on immune function; however, these are rarely appreciable from single timepoint measurements. Longitudinal sampling also enables inferences to be made regarding gene or protein expression over time and their possible causal relationships and dependencies. However, a key consideration when making such inferences from longitudinal samples is to minimize the technical sources of variation, such that any changes in cell population size or functional state are accurately reflected with minimal noise. Examples of factors to consider to minimize technical variation include the use of standardized protocols for sample collection and processing, and striving to run as many samples/cells together to reduce batch effects from any assays performed. It is particularly important to combine all longitudinal samples from a given a subject in the same batch, ensuring that any comparative groups are equally represented in each experimental batch. Also, reagents such as stimulation cocktails or staining antibody cocktails should be prepared all at once rather than for each experiment; thus, such known sources of technical variation might be minimized. Importantly, an analysis plan for what types of inference methods or modeling approaches are to be used must be already defined at the experimental design stage, and ideally prior to sample collection, to optimally consider strategies for minimized technical variation, maximal sensitivity, sufficient statistical power in analyses, and maximized opportunities for clear biological conclusions. A common mistake is to collect samples first and then try to solve analytical challenges once all the data are obtained, often leading to insignificant results and difficulties in drawing clear conclusions from complex datasets. In summary, we hope that we have provided convincing evidence regarding the advantages of taking a step back and studying the immune system as a whole, proposing suitable methodological approaches to capture biological data for systemic inferences, such as longitudinal whole-blood sampling. We have also highlighted a few of the many recent advances and outlined possible next steps to make the immune system more interpretable through systems immunology, such as focusing on cell–cell interactions and modeling the cell–cell population dynamics. Although implementing systems immunology may seem like a daunting task (see Outstanding questions), we believe that by doing so many of the mysteries surrounding immune function might finally be solved. This can have an important clinical impact on vaccine development and administration, infectious diseases, autoimmune conditions, cancer, and more.Outstanding questionsWhat measurement type and combination of measurements provide the most informative datasets for understanding immune-cell dependency and regulation?What inference methods are optimal for modeling cell–cell interactions, and how much data is required?How generalizable are inferred cell–cell regulatory mechanisms across individual populations of different ages, sexes, and ancestries? What measurement type and combination of measurements provide the most informative datasets for understanding immune-cell dependency and regulation? What inference methods are optimal for modeling cell–cell interactions, and how much data is required? How generalizable are inferred cell–cell regulatory mechanisms across individual populations of different ages, sexes, and ancestries? Funding sources are Knut och Alice Wallenbergs Stiftelse Award Number 2019.0181 to P.B.; Vetenskapsrådet Award Numbers 2022-01567, 2021-06529, 2020-06190, 2020-02889, 2018-06228, 2019-01495, 2018-06228 to P.B.; and Svenska Sällskapet för Medicinsk Forskning Award Number CG-22-0148-H-02 to P.B. P.B. is a founder of Cytodelics AB (Stockholm, Sweden), executive board member of Kancera AB (Stockholm, Sweden), scientific advisor of Pixelgen Technologies AB (Stockholm, Sweden), Scailyte AG (Basel, Switzerland) and Oxford Immune Algorithmics Inc. (Oxford, UK) and Infinant Health Inc. (Davis, CA, USA).